code: def setup_tokenizer(nemo_model_file):
      """TOKENIZER SETUP
      nemo_model_file (str): The path to the NeMo model file (.nemo).
      """
      logging.info(f"Loading nemo model '{nemo_model_file}' ...")
      if nemo_model_file.endswith(".nemo"):
          model = nemo_asr.models.ASRModel.restore_from(
              nemo_model_file, map_location=torch.device("cpu")
          )
      else:
          logging.warning(
              "tokenizer_model_file does not end with .model or .nemo, therefore trying to load a pretrained model with this name."
          )
          model = nemo_asr.models.ASRModel.from_pretrained(
              nemo_model_file, map_location=torch.device("cpu")
          )
  
      is_aggregate_tokenizer = False
      tokenizer_nemo = None
      encoding_level = SUPPORTED_MODELS.get(type(model).__name__, None)
      if not encoding_level:
          logging.warning(
              f"Model type '{type(model).__name__}' may not be supported. Would try to train a char-level LM."
          )
          encoding_level = "char"
  
      if encoding_level == "subword":
          if type(model.tokenizer).__name__ == "AggregateTokenizer":
              is_aggregate_tokenizer = True
  
          tokenizer_nemo = model.tokenizer
  
      del model
  
      return tokenizer_nemo, encoding_level, is_aggregate_tokenizer
  
file_path: NeMo/scripts/asr_language_modeling/ngram_lm/kenlm_utils.py
imports: import gzip
  import json
  import os
  import numpy
  import torch
  from joblib import Parallel, delayed
  from tqdm.auto import tqdm
  import nemo.collections.asr
  from nemo.collections.asr.parts.submodules.ctc_beam_decoding import DEFAULT_TOKEN_OFFSET
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling a global function with example usage: setup_tokenizer(nemo_model_file) and returns: tokenizer_nemo,  encoding_level,  is_aggregate_tokenizer